Links to access the dataset mentioned in the paper:The Stanford Twitter Sentiment Test (STSTd) data...

Question

Links to access the dataset mentioned in the paper:

The Stanford Twitter Sentiment Test (STSTd) data set
https://www.kaggle.com/datasets/kazanova/sentiment140
Sentiment140 dataset with 1.6 million tweets | Kaggle
Sentiment analysis with tweets
www.kaggle.com
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The SE2014 datasethttps://alt.qcri.org/semeval2014/task9/index.php?id=data-and-tools#
The Stanford Twitter Sentiment Gold (STSGd) data set
https://www.kaggle.com/datasets/divyansh22/stsgold-dataset

STS-Gold Dataset | Kaggle

A smaller yet powerful Tweet dataset . This dataset is originally prepared by Saif et. al XXXXXXXXXXPlease cite the paper if you intend to use this dataset.

www.kaggle.com

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The Sentiment Strength Twitter dataset (SSTd)
http://sentistrength.wlv.ac.uk/documentation/6humanCodedDataSets.zip

You can find some other datasets at the following link:

https://github.com/pmbaumgartner/text-feat-lib

Amar Kumar · Accepted Answer

Sentiment analysis with a deep convolution neural network on Twitter
For separating tweets into three categories, we propose a deep convolutional neural network in this study:positive, neutral, and negativeThe concepts presented by Kim, Collobert, and coworkers serve as the foundation for our plan, which incorporates both designs. Kim provides a framework that applies a parameter with a different window magnitude the provided text. By introducing two completely connected layers with dropout and a softmax layer into the design, we modify the aforementioned model. Since using a linear layer produced subpar results, the first is made up of sigmoid activated units. In the second layer, a standard softmax layer comes after the hyperbolic tangent layers.
CNNs that perform pooling operations take into account the context in which each word appears as well as the order in which the words are arranged because they naturally deal with sentences of varying lengths. This solves the problem with negations, which can appear anywhere in a statement. We treat each tweet as a single phrase for simplicity's sake. The architecture of the model is shown, and it resembles the one that is mentioned in
Let's examine a tweet that is tokens long and properly padded at the beginning and end. The padding length is calculated using the formula h/2, where h is the filter's window size. In the first step, a lookup table called LRk×|V| is used to map tokens to the right word vectors. Here, Vis and ki are the word vectors' dimensions and the vocabulary of the words in the lookup table, respectively. A vector wi∈Rk is projected for every word or token. After the mapping process, a tweet is therefore represented as a concatenation of word embeddings.
The succeeding process applies a variety of filters with various window sizes during the convolution step. When filters are applied to each window of words in the tweet, a feature map is created. For each of the filters, a weight matrix Wc∈Rhu×hk and a bias term b are learned, where hui is the convolutional layer's hidden unit count. The local properties around each word window are extracted using the weight matrix. The formal definition of the convolution operation is
		Deep convolutional neural network architecture
In which xi:i+h−1 The hyperbolic tangent function is h(·), and  i + h − 1. Consists of the word vectors from position I to position I plus h concatenated. The feature map x 0 created by the convolution procedure is then subjected to a max-over-time pooling approach.
As a result,

Links to access the dataset mentioned in the paper: The Stanford Twitter Sentiment Test (STSTd) data set https://www.kaggle.com/datasets/kazanova/sentiment140 Sentiment140 dataset with 1.6 million...

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